dwarf-expert▌
trailofbits/skills · updated Apr 8, 2026
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Technical expertise for analyzing DWARF debug files and understanding the DWARF standard (v3–v5).
- ›Covers DWARF standard questions, parsing debug information from binaries, and code that interacts with DWARF data
- ›Provides verification workflows using llvm-dwarfdump --verify for structural validation and quality metrics
- ›Supports parsing tools including dwarfdump , readelf , and DWARF parsing libraries (libdwarf, pyelftools, gimli)
- ›References authoritative sources: official DWARF sta
Overview
This skill provides technical knowledge and expertise about the DWARF standard and how to interact with DWARF files. Tasks include answering questions about the DWARF standard, providing examples of various DWARF features, parsing and/or creating DWARF files, and writing/modifying/analyzing code that interacts with DWARF data.
When to Use This Skill
- Understanding or parsing DWARF debug information from compiled binaries
- Answering questions about the DWARF standard (v3, v4, v5)
- Writing or reviewing code that interacts with DWARF data
- Using
dwarfdumporreadelfto extract debug information - Verifying DWARF data integrity with
llvm-dwarfdump --verify - Working with DWARF parsing libraries (libdwarf, pyelftools, gimli, etc.)
When NOT to Use This Skill
- DWARF v1/v2 Analysis: Expertise limited to versions 3, 4, and 5.
- General ELF Parsing: Use standard ELF tools if DWARF data isn't needed.
- Executable Debugging: Use dedicated debugging tools (gdb, lldb, etc) for debugging executable code/runtime behavior.
- Binary Reverse Engineering: Use dedicated RE tools (Ghidra, IDA) unless specifically analyzing DWARF sections.
- Compiler Debugging: DWARF generation issues are compiler-specific, not covered here.
Authoritative Sources
When specific DWARF standard information is needed, use these authoritative sources:
-
Official DWARF Standards (dwarfstd.org): Use web search to find specific sections of the official DWARF specification at dwarfstd.org. Search queries like "DWARF5 DW_TAG_subprogram attributes site:dwarfstd.org" are effective.
-
LLVM DWARF Implementation: The LLVM project's DWARF handling code at
llvm/lib/DebugInfo/DWARF/serves as a reliable reference implementation. Key files include:DWARFDie.cpp- DIE handling and attribute accessDWARFUnit.cpp- Compilation unit parsingDWARFDebugLine.cpp- Line number informationDWARFVerifier.cpp- Validation logic
-
libdwarf: The reference C implementation at github.com/davea42/libdwarf-code provides detailed handling of DWARF data structures.
Verification Workflows
Use llvm-dwarfdump verification options to validate DWARF data integrity:
Structural Validation
# Verify DWARF structure (compile units, DIE relationships, address ranges)
llvm-dwarfdump --verify <binary>
# Detailed error output with summary
llvm-dwarfdump --verify --error-display=full <binary>
# Machine-readable JSON error summary
llvm-dwarfdump --verify --verify-json=errors.json <binary>
Quality Metrics
# Output debug info quality metrics as JSON
llvm-dwarfdump --statistics <binary>
The --statistics output helps compare debug info quality across compiler versions and optimization levels.
Common Verification Patterns
- After compilation: Verify binaries have valid DWARF before distribution
- Comparing builds: Use
--statisticsto detect debug info quality regressions - Debugging debuggers: Identify malformed DWARF causing debugger issues
- DWARF tool development: Validate parser output against known-good binaries
Parsing DWARF Debug Information
readelf
ELF files can be parsed via the readelf command ({baseDir}/reference/readelf.md). Use this for general ELF information, but prefer dwarfdump for DWARF-specific parsing.
dwarfdump
DWARF files can be parsed via the dwarfdump command, which is more effective at parsing and displaying complex DWARF information than readelf and should be used for most DWARF parsing tasks ({baseDir}/reference/dwarfdump.md).
Working With Code
This skill supports writing, modifying, and reviewing code that interacts with DWARF data. This may involve code that parses DWARF debug data from scratch or code that leverages libraries to parse and interact with DWARF data ({baseDir}/reference/coding.md).
Choosing Your Approach
┌─ Need to verify DWARF data integrity?
│ └─ Use `llvm-dwarfdump --verify` (see Verification Workflows above)
├─ Need to answer questions about the DWARF standard?
│ └─ Search dwarfstd.org or reference LLVM/libdwarf source
├─ Need simple section dump or general ELF info?
│ └─ Use `readelf` ({baseDir}/reference/readelf.md)
├─ Need to parse, search, and/or dump DWARF DIE nodes?
│ └─ Use `dwarfdump` ({baseDir}/reference/dwarfdump.md)
└─ Need to write, modify, or review code that interacts with DWARF data?
└─ Refer to the coding reference ({baseDir}/reference/coding.md)
How to use dwarf-expert on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add dwarf-expert
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches dwarf-expert from GitHub repository trailofbits/skills and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate dwarf-expert. Access the skill through slash commands (e.g., /dwarf-expert) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★54 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
I recommend dwarf-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Nikhil Chen· Dec 28, 2024
Registry listing for dwarf-expert matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Yuki Robinson· Dec 24, 2024
Useful defaults in dwarf-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Nikhil Harris· Dec 20, 2024
Useful defaults in dwarf-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Oshnikdeep· Nov 19, 2024
Useful defaults in dwarf-expert — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Nikhil Sanchez· Nov 15, 2024
I recommend dwarf-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Kiara Iyer· Nov 11, 2024
I recommend dwarf-expert for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Ganesh Mohane· Oct 10, 2024
dwarf-expert is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Nikhil Nasser· Oct 6, 2024
dwarf-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Anika Ramirez· Oct 2, 2024
dwarf-expert reduced setup friction for our internal harness; good balance of opinion and flexibility.
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